print.DNN_predictor

This function prints the number of factors suggested by the deep neural network (DNN) predictor.

Provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>, Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis (PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.

Haijiang Qin

EFAfactors

Determining the Number of Factors in Exploratory Factor Analysis

Lei Guo

print.DNN_predictor function

<dl><dt>x</dt>
<dd>An object of class <code>DNN_predictor</code>, representing the results to be printed.</dd>
<dt>...</dt>
<dd>Additional arguments to be passed to the plotting function.</dd></dl>

Arguments

Print DNN Predictor Method Results — print.DNN_predictor

<dl>

<dt>x</dt>
<dd>An object of class <code>DNN_predictor</code>, representing the results to be printed.</dd>


<dt>...</dt>
<dd>Additional arguments to be passed to the plotting function.</dd>

</dl>

print.DNN_predictor: Print DNN Predictor Method Results

Description

Usage

Value

Arguments

See Also